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Comparison

horovod vs ray

Verdict

Pick horovod if simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes; pick ray if ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads.

Markdown twin · horovod alternatives · ray alternatives

GraphCanon updated Sep 26, 2026

8views this month

horovod logo

horovod

horovod/horovod

★ 15kpushed Sep 17, 2026
vs
ray logo

ray

ray-project/ray

★ 44kpushed Sep 18, 2026

Trust & integrity

Signalhorovodray
Maintenance
Archived (8d since push)
As of Sep 26, 2026 · github_public_v1
Very active (0d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 26, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 18, 2026 · osv@v1
deps.dev advisories
No lockfile (source not queried)
As of Aug 2, 2026 · deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Published findings
As of Jul 26, 2026 · openssf-scorecard@v1
Not queried
openssf-scorecard@v1
horovod trust reportray trust reportTrust methodology

Tagline

horovod
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
ray
Unified framework for scaling AI and Python applications

Stars

horovod
15k
ray
44k

Forks

horovod
2.2k
ray
8.1k

Open issues

horovod
407
ray
3.6k

Language

horovod
Python
ray
Python

Adopt for

horovod
Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes.
ray
Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads.

Persona

horovod
-
ray
-

Runtime

horovod
-
ray
-

License

horovod
Other
ray
Apache-2.0

Last pushed

horovod
Sep 17, 2026
ray
Sep 18, 2026

Categories

horovod
Model Training
ray
Developer Tools, Evaluation & Observability, Inference & Serving, Model Training

Trust and health

Maintenance

horovod
Archived (8%)
ray
Very active (96%)

Days since push

horovod
8d
ray
0d

Archived on GitHub

horovod
Yes
ray
No

Open issues (now)

horovod
407
ray
3.6k

Stars delta

horovod
-13 (30d)
ray
+337 (30d)

Open issues delta

horovod
+1 (30d)
ray
+107 (30d)

deps.dev advisories

horovod
No lockfile (source not queried)
ray
Not queried

OpenSSF Scorecard

horovod
Published findings
ray
Not queried

Full report

horovod
Trust report
ray
Trust report

Choose horovod if…

  • License: horovod is Other, ray is Apache-2.0.
  • Tags unique to horovod: distributed training, keras, mxnet, pytorch.
  • When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts.

When NOT to use horovod

  • Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility.
  • Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch.
GitHubDetails

Choose ray if…

  • License: ray is Apache-2.0, horovod is Other.
  • Tags unique to ray: data-science, deployment, distributed, hyperparameter-optimization.
  • Also covers Developer Tools, Evaluation & Observability, Inference & Serving.
  • When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs.

When NOT to use ray

  • If your project is limited to single-node development and does not require scaling across multiple machines or clusters.
  • When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU
  • If you are working in a non-Python environment, as Ray is primarily designed for Python applications.
GitHubDetails

Explore

horovod trust report →ray trust report →Model Training category →Developer Tools category →Evaluation & Observability category →Inference & Serving category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: horovod 15k · ray 44k (synced Sep 26, 2026).

Common questions

What is the difference between horovod and ray?
horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.. ray: Unified framework for scaling AI and Python applications. See the comparison table for live GitHub stats and shared categories.
When should I choose horovod over ray?
Choose horovod over ray when License: horovod is Other, ray is Apache-2.0; Tags unique to horovod: distributed training, keras, mxnet, pytorch; When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts.
When should I choose ray over horovod?
Choose ray over horovod when License: ray is Apache-2.0, horovod is Other; Tags unique to ray: data-science, deployment, distributed, hyperparameter-optimization; Also covers Developer Tools, Evaluation & Observability, Inference & Serving; When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs.
When should I avoid horovod?
Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility. Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch.
When should I avoid ray?
If your project is limited to single-node development and does not require scaling across multiple machines or clusters. When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU If you are working in a non-Python environment, as Ray is primarily designed for Python applications.
Is horovod or ray more popular on GitHub?
ray has more GitHub stars (43,863 vs 14,682). Stars measure visibility, not whether either tool fits your constraints.
Are horovod and ray open source?
Yes - both are open-source projects on GitHub (horovod: Other, ray: Apache-2.0).
Where can I find alternatives to horovod or ray?
GraphCanon lists graph-backed alternatives at horovod alternatives and ray alternatives (horovod markdown twin, ray markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, horovod or ray?
horovod: Archived. ray: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for horovod and ray?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: horovod trust report; ray trust report.

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